This document discusses how companies can make advanced analytics work for them. It recommends that companies (1) choose the right data by focusing on specific business problems and considering new sources, (2) build models that predict and optimize business outcomes through hypothesis-led modeling and simple models, and (3) transform capabilities by developing business-relevant analytics, embedding analytics in tools, and building skills. The document also notes challenges like modeling errors, behavioral inertia, and weak analytical skills, and advises that companies act now on targeted efforts rather than overhauls.

1. Making Advanced Analytics
Work for You
Based on article of the same name by Dominic Barton and David Court

2. Data Analytics:
A competitive differential

3. Data Analytics:
A competitive differential
According to research by
Andrew McAfee and
Erik Brynjolfsson,
of MIT, companies that inject
big data and
analytics
into their operations show
productivity rates and
profitability that are
5% to 6% higher
than those of their peers
14
8
PROFITABILITY
big data advantage ordinary

4. Insight #1:
How to
go about
it ?

5. Choose the
Right Data
Build Models That
Predict and
Optimize Business
Outcomes Transform Your
Company’s
Capabilities

6. Choose the
Right Data
• Be specific about business
problems you want to solve
• Acknowledge the potential of
external and new
sources of data- e.g. social
media
• Acquisition of data assets
• Ask, “What decisions
could we make if we had
all the information we
need?”
Choose the
Right Data

7. Build Models That
Predict and
Optimize Business
Outcomes
Build Models That
Predict and
Optimize Business
Outcomes
• Hypothesis led
modelling
• Attune models
according to
executional ease
• Ask, “What’s the
least complex
model that would
improve our
performance?”
A pure
data-mining
approach is
often futile

8. Transform Your
Company’s
Capabilities
Issue:
a mismatch
between the
organization’s
existing
culture and
capabilities
and the
emerging
tactics to
exploit
analytics
successfully
1.Develop
business-
relevant
analytics that
can be put to
use.
2.Embed analytics
into simple
tools for the
front lines
3.Develop
capabilities to
exploit big
data- training,
team approach,
incentives

9. Insight #2:Challenges

10. 1.Modelling error and
adaptation cost
2.Behavioural inertia
3.Weak analytical
acumen on the
frontline

11. Managerial Relevance

12. 1.Impresses the importance of analytics as
a competitive differential
2.Prepares the manager to look for
benchmarks to make her organization
analytics-able
3.Highlights challenges
Why?

13. Way forward for the Manager
The era of big data is evolving rapidly, and it is
recommended that most companies should act now.
Rather than undertaking massive overhauls of their
companies, executives should concentrate on
targeted efforts to source data, build models,
and transform the organizational culture.
Such efforts will play a part in maintaining
flexibility.

14. Thank You